Quantifying Urban Expansion Using Landsat Images and Landscape Metrics: A Case Study of the Halton Region, Ontario

GEOMATICA ◽  
2020 ◽  
Author(s):  
Liyuan Qing ◽  
Hasti A. Petrosian ◽  
Sarah N. Fatholahi ◽  
Michael A. Chapman ◽  
Jonathan Li
Keyword(s):  
Landscape Metrics ◽  
Urban Areas ◽  
Census Data ◽  
Urban Expansion ◽  
Landsat Images ◽  
Factors Affecting ◽  
Supervised Learning Algorithms ◽  
Dramatic Rise ◽  
Multi Temporal ◽  
Gis Software

Urbanization is considered as one of the main factors affecting global change. The Halton Region as part of the Great Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national GDP. It is also one of the most desirable places for living and thriving business. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches and landscape metrics. Multi-temporal Landsat images, and the supervised learning algorithms in GIS software were used to explore the dynamic changes, and to classify the urban and non-urban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods, including Land Use in Central Indiana (LUCI) model, Vegetation-Impervious Surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of driven forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating leapfrog pattern of urbanization occurred over the entire period. The contribution of this research is to evaluate the urbanization in the Halton Region, and give the city managers a clear mind to make appropriate decisions in further urban planning.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

2014 ◽  
Vol 5 (2) ◽  
pp. 19-37 ◽  
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Small Towns ◽  
Temporal Data ◽  
Landsat Images ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

scholarly journals Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea

2019 ◽  
Vol 9 (6) ◽  
pp. 1098 ◽  
Author(s):  
Yun-Jae Choung ◽  
Jin-Man Kim
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
South Korea ◽  
Urban Areas ◽  
Urban Expansion ◽  
Landsat Images ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Multi Temporal ◽  
The Relationship

To protect the population from respiratory diseases and to prevent the damages due to air pollution, the main cause of air pollution should be identified. This research assessed the relationship between the airborne particulate concentrations (PM10) and the urban expansion in Daegu City in South Korea from 2007 to 2017 using multi-temporal spatial datasets (Landsat images, measured PM10 data) and the machine learning technique in the following steps. First, the expanded urban areas were detected from the multiple Landsat images using support vector machine (SVM), a widely used machine learning technique. Next, the annual PM10 concentrations were calculated using the long-term measured PM10 data. Finally, the degrees of increase of the expanded urban areas and of the PM10 concentrations in Daegu from 2007 to 2017 were calculated by counting the pixels representing the expanded urban areas and computing variation of the annual PM10 concentrations, respectively. The experiment results showed that there is a minimal or even no relationship at all between the urban expansion and the PM10 concentrations because the urban areas expanded by 55.27 km2 but the annual PM10 concentrations decreased by 17.37 μg/m³ in Daegu from 2007 to 2017.

Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2019 ◽  
pp. 1372-1382
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

Integration of Satellite Remote Sensing Techniques and Landscape Metrics to Characterize Land Cover Change and Dynamics

2013 ◽  
pp. 228-244
Author(s):  
Carmelo Riccardo Fichera ◽  
Giuseppe Modica ◽  
Maurizio Pollino
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Landscape Metrics ◽  
Landsat Images ◽  
Aerial Photos ◽  
Multi Temporal ◽  
Urban Rural ◽  
Remote Sensing Techniques

One of the most relevant applications of Remote Sensing (RS) techniques is related to the analysis and the characterization of Land Cover (LC) and its change, very useful to efficiently undertake land planning and management policies. Here, a case study is described, conducted in the area of Avellino (Southern Italy) by means of RS in combination with GIS and landscape metrics. A multi-temporal dataset of RS imagery has been used: aerial photos (1954, 1974, 1990), Landsat images (MSS 1975, TM 1985 and 1993, ETM+ 2004), and digital orthophotos (1994 and 2006). To characterize the dynamics of changes during a fifty year period (1954-2004), the approach has integrated temporal trend analysis and landscape metrics, focusing on the urban-rural gradient. Aerial photos and satellite images have been classified to obtain maps of LC changes, for fixed intervals: 1954-1985 and 1985-2004. LC pattern and its change are linked to both natural and social processes, whose driving role has been clearly demonstrated in the case analysed. In fact, after the disastrous Irpinia earthquake (1980), the local specific zoning laws and urban plans have significantly addressed landscape changes.

scholarly journals Application of remote sensing and GIS for assessing the urbanization trend in Can Tho city

2018 ◽  
Vol 2 (1) ◽  
pp. 52-57
Author(s):  
Le Van Trung ◽  
Nguyen Nguyen Vu
Keyword(s):  
Remote Sensing ◽  
Urban Expansion ◽  
Impervious Surface ◽  
Remote Sensing And Gis ◽  
Growth Patterns ◽  
Landsat Images ◽  
Average Growth Rate ◽  
Average Growth ◽  
Multi Temporal ◽  
Can Tho City

This paper presents the method of integrating remote sensing and GIS to analyze the urbanization trend through the impervious surface change in Can Tho City. The impervious surface maps were created from the multi-temporal LandSat images in 1997, 2005, 2010, 2016 and were overlaid in GIS to extract the urban expansion from 1997 to 2016. The results showed the urban area of Can Tho increased from 1506,638 ha in 1997 to 5611,114 ha in 2016, average growth rate of 14,3%/year. The integration of remote sensing and GIS was found to be effective in monitoring and analyzing urban growth patterns.

scholarly journals THE STUDY OF HEAT ISLAND AND ITS RELATION WITH URBANIZATION IN GURUGRAM, DELHI NCR FOR THE PERIOD OF 1990 TO 2018

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 49-56
Author(s):  
S. Kushwaha ◽  
Y. Nithiyanandam
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Expansion ◽  
Large City ◽  
Capital City ◽  
Spatial Decision Support System ◽  
Heat Island ◽  
Landsat Images ◽  
Infrared Band ◽  
Major Satellite

Abstract. Rapid growth in population and land cover makes urban areas more vulnerable to Urban Heat Island. Due to which, cities experience higher mean temperature than its proximate surrounding rural or non-urban area. The relationship between UHI and urbanization is proven in previous studies. Delhi the capital city of India is well known for its extreme heat condition in summer and air pollution. In this study, an attempt has been made to understand UHI behavior in a satellite town of Delhi. Satellite town or cities are the small independent towns built in the vicinity of a large city or metropolitan city. In this paper 4 major satellite towns of Delhi, i.e. Gurugram (name changed from Gurgaon in April 2016), Noida, Faridabad and Ghaziabad has been studied to understand the changing trends in urbanization and temperature. The parameters used are rate of urban expansion, population density, GDP growth and increasing temperature over the last two decades. Gurugram showed the maximum urbanization and identified as study area. Gurugram has undergone a major growth journey from being a small town to ‘The Millennium city’ of the country in a short span. The Landsat images of past three decades ranging from different time period i.e. 1990, 1996, 2002, 2009, 2014 and 2018 were investigated by applying integrated approach of GIS and Remote sensing. The images represent the condition of UHI and urbanization in different period. The temporal change in LULC was used to study the rate of urban growth in last three decades. The results showed the increase in built-up area out of the total area of Gurugram from 10% (i.e.50.6 sq. km) in 1990 to 17.25% (80.5 sq. km) in 2002 which further increased to 45.1% (210.4 sq. km) in 2018. Thermal Infrared band of Landsat series were used to retrieve land surface temperature (LST) intensity of the study period. The results show a positive correlation (r = 0.46) between impervious surfaces and LST. The results of the study could be helpful in identifying the causative factors and level of impacts in different zones and also enable us to develop a mitigation strategy based on spatial decision support system.

scholarly journals Geoinformatics Based Sprawl Dynamics Analysis of Berhampur Development Authority Area, Odisha, India

2019 ◽  
Vol 8 (4) ◽  
pp. 10471-10477
Keyword(s):  
Urban Areas ◽  
Satellite Images ◽  
Spatial Information ◽  
Spatial Dimension ◽  
High Resolution Satellite Images ◽  
Multi Temporal ◽  
Authority Area ◽  
Arc Gis ◽  
Spatio Temporal ◽  
Gis Software

Urban and Regional planners need accurate and authentic spatio-temporal information of urban sprawls for efficient and sustainable planning of towns & cities worldwide. Geoinformatics powered with temporal high resolution satellite images, Geographic Information System (GIS), mobile technology, etc is now emerged as the most powerful tool for mapping and monitoring the sprawls of urban habitations. In this paper an attempt is made for analysing the dynamics of sprawls of three statutory towns of Berhampur Development Authority (BeDA) area of Ganjam District, Odisha state, India. The spatial information of urban sprawl of each town has been generated using openly available toposheets and multi -sensor & multi - temporal satellite images and the spatio temporal characteristics of sprawls has been analysed in Arc GIS software. The sprawl area as well as the population of the three towns have been analysed and the future scenario of sprawl-population dynamics has been forecasted for the years 2021 and 2031.The result of this paper highlights that sprawls of the three towns i.e Berhampur, Chhatrapur and Gopalpur will expand their spatial dimension by 22,18 and 97 percent by 2031 whereas population of the three towns will increase by 43, 19 and 15 percent between 2011 -2031.Finally the result indicates that there will be decrease in population density in the three towns which will ultimately force the Development Authority to plan more basic infrastructures and transportation in the newly expanded urban areas.

scholarly journals CONTINUOUS CHANGE DETECTION OF URBAN LAKES IN WUHAN, CHINA USING MULTI-TEMPORAL REMOTE SENSING IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 2315-2320
Author(s):  
W. Zhang ◽  
X. Kong ◽  
G. Tan ◽  
S. Zheng
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Central Area ◽  
Strong Impact ◽  
Continuous Change ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Urban Lakes ◽  
Multi Temporal ◽  
Lake Change

Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37&amp;thinsp;km<sup>2</sup> between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.

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